Well System Electrical Submersible Pump Equipment Failure Analysis

Failure analysis performed on electrical submersible pump equipment includes evaluating the condition of the equipment at multiple stages, including assembly, testing, installation, removal, and when dismantled. Knowledge of causes and interpretation of damage and/or wear incurred is limited to domain subject matter experts who have previous experience with particular conditions relevant to a particular failure, thus limiting the number of people who may properly diagnose failures.

The description that follows includes example systems, methods, techniques, and program flows that embody aspects of the disclosure. However, it is understood that this disclosure may be practiced without these specific details. In some instances, well-known instruction instances, protocols, structures, and techniques have not been shown in detail in order not to obfuscate the description.

Electrical submersible pumps and related equipment (“ESP equipment”) have data associated with them that may be useful in diagnosing problems. For example, ESP equipment may have specifications, the well system(s) in which the ESP equipment is used may have various features (e.g., depth, operating pressures, etc.), and reliability reports may be created in response to previous ESP equipment failures. This and other data may be useful in determining the cause of an ESP equipment failure.

In some implementations, one or more machine learning models can be trained on the data associated with ESP equipment to help improve failure analysis of ESP equipment. The data may include reliability reports created during previous failure analyses, images associated with the ESP equipment, ESP equipment specifications, information about ESP equipment usage conditions, etc. When subsequent failures occur to ESP equipment, images of the failed ESP equipment can be provided as input to the machine learning models. The machine learning models can categorize the images based on the subject matter contained therein and may generate captions for the images indicating various properties of the subject matter contained therein.

In some implementations, a failure diagnostic tool is based, at least in part, on one or more machine learning models. Each machine learning model can be trained on data related to ESP equipment, such as reliability reports and images (including photographs) of ESP equipment. The failure diagnostic tool may be used by engineers to assist in identifying types of damage and/or wear and the cause(s) of the failure. For example, an engineer may use a photograph taken of one or more pieces of ESP equipment during disassembly as input to the failure diagnostic tool. The failure diagnostic tool may generate one or more potential captions based, at least in part, on the subject matter in the photograph and may also generate indications of one or more potential causes of failure.

In some implementations, the one or more machine learning models implement operations to categorize images associated with the ESP equipment into one or more categories. The images may be photographs taken of the ESP equipment from one or more points in the life of the ESP equipment, including assembly, testing, installation, removal, dismantling/disassembly, etc.

In some implementations, the one or more machine learning models implement operations to identify indications of wear and/or damage and/or one or more causes of failure based, at least in part, on one or more images. The images may be photographs taken of the ESP equipment from one or more points in the life of the ESP equipment, including assembly, testing, installation, removal, dismantling/disassembly, etc.

In some implementations, a library of images and associated data (e.g., image captions) is used to train one or more machine learning models (e.g., a deep-learning models) that can work with image and text data. Some examples of machine learning models that can work with image and text data include long-short-term-memory models, recurrent neural network models, convolution neural network models, etc. Each image has one or more captions associated with it and the chosen machine learning model is capable of determining the meaning of the caption using natural language processing (NLP) techniques. The NLP techniques may be implemented by the one or more machine learning models or implemented by a separate module working in conjunction with the one or more machine learning models. The chosen machine learning model is capable of reading images of varying size and with a color dimension.

In some implementations, a machine learning model is trained using data that has been categorized into a plurality of categories. The categories may be pre-defined or may be determined using a machine learning model, deep learning algorithm, etc. The trained machine learning model is then usable to categorize each image in the library of images into one or more categories of the plurality of categories. For example, the plurality of categories may include the subject of the image, the stage of the drilling job, etc. The machine learning model (or a related module) may utilize NLP (such as sentiment analysis), object detection, classification, and other techniques to categorize the images.

In some implementations, once the one or more machine learning models are trained on the images and associated data, additional images may be provided as input to the one or more machine learning models which may then perform one or more operations to categorize the images, generate one or more captions for the images, generate a failure cause based, at least in part, on the images, etc. The one or more machine learning models may also generate an instance of a confidence metric associated with each of the results.

An example implementation might comprise a set of training data associated with ESP equipment and an untrained categorizing machine learning model. The training data may comprise a large variety of data associated with ESP equipment. In this example, the training data falls into at least four broad categories: images (including photographs) of ESP equipment, ESP equipment specifications, well system parameters, and reliability reports. The reliability reports may combine the equipment specifications, well system parameters, and the images of the ESP equipment with other graphical and textual data, such as image captions, descriptions of equipment conditions/wear/damage, plots of ESP equipment operational history, etc.

The untrained categorizing machine learning model is trained to categorize previously unseen images of the ESP equipment into one or more categories of a plurality of categories based, at least in part, on the reliability reports, ESP equipment specifications, or the well system parameters. The training of the untrained categorizing machine learning model results in a trained categorizing machine learning model that can take previously unseen images of ESP equipment and categorize them according to one or more categories of the plurality of categories. The training process may utilize techniques such as object detection and NLP to assist in identifying relationships usable to categorize the images of the ESP equipment.

For example, assume the training data consists of a large number of reliability reports and the reliability reports frequently include photographs of intake screens removed from electrical submersible pumps with captions like “intake screen clear of debris” or “intake screen clogged with debris”. The training process may utilize text recognition techniques to read the captions from the images and NLP techniques to parse the captions. The training process may also use image processing techniques, object detection techniques, and the like to train the untrained categorizing machine learning model to identify intake filters in the photographs of the ESP equipment. The training process may further train the untrained categorizing machine learning model to identify whether the intake filters in the photographs of the ESP equipment are clear or clogged by utilizing the parsed captions. The trained categorizing machine learning model may thus be able to take a previously unseen photograph of ESP equipment and categorize the photograph as a clogged intake screen or unclogged intake screen.

As another example, the reliability reports may include model numbers associated with the individual components of an electrical submersible pump. The training process may result in a trained categorizing machine learning model that is capable of categorizing previously unseen images of ESP equipment into categories corresponding to the model(s) of the electrical submersible pump shown in the images.

As another example, the reliability reports may include failure causes that specify the reasons ESP equipment has failed or may have failed in the future. The training process may result in a trained categorizing machine learning model that is capable of categorizing previously unseen images of ESP equipment into categories corresponding to failure causes that are typically associated with features of the images of the ESP equipment. For example, if images of clogged intake screens frequently appear in reliability reports that have a failure cause of “improper sand size,” the trained categorizing machine learning model may categorize previously unseen images of clogged intake screens as being associated with improper sand sizes.

The plurality of categories may be pre-determined or may be created using automated methods. For example, instead of including pre-determined categories as part of the training data, the training process may be configured to cluster the photographs of the ESP and then the clusters may be used as the plurality of categories.

The example implementation might also comprise an untrained caption-generating machine learning model. The untrained caption-generating machine learning model is trained to generate one or more captions for previously unseen images of ESP equipment. The training of the untrained caption-generating machine learning model can use the same training data as used to train the untrained categorizing machine learning model or different training data. For example, the training data for training the untrained caption-generating machine learning model may be images of ESP equipment along with one or more captions corresponding to one or more of the images of the ESP equipment.

Captions may come in different forms. For example, in some instances, captions may be text located in close proximity to an image embedded in a document, such as a line of text directly under the image and separated from the main body text. In some instances, captions may be text overlaid on the original image. In some instances, captions may be embedded into metadata associated with the image.

When the captions are text overlaid on the original image, optical character recognition, NLP, and other techniques can be used to identify the text in the image and turn the captions into computer-readable text.

NLP techniques may further be used to transform the captions for use as training data or may be incorporated in the untrained caption-generating machine learning model itself.

After the training process, the trained caption-generating machine learning model may be capable of performing one or more operations to generate captions for previously unseen images of ESP equipment. For example, if the images of ESP equipment frequently contain photographs of clogged intake screens with captions like “intake screen clogged with debris,” the trained caption-generating machine learning model may be capable of receiving a previously unseen and uncaptioned image of a clogged intake screen and generate the caption “intake screen clogged with debris.”

The caption-generating machine learning model may be implemented to work based on the plurality of categories, based on the images of the ESP equipment directly, a combination thereof, or using other data. For example, in some implementations, the caption-generating machine learning model may be implemented such that it receives the output of the trained categorizing machine learning model (one or more categories of the plurality of categories) and generates a caption based on the output of the trained categorizing machine learning model. As another example, in some implementations, the caption-generating machine learning model may be implemented to receive an image of ESP equipment and generate a caption based on the image of the ESP equipment.

The trained caption-generating machine learning model may generate the captions as human readable text, which may then be associated with the input image (e.g., using a data structure like a map or by adding the text as an overlay on the original image).

The example implementation might also comprise an untrained failure cause-generating machine learning model. The untrained failure cause-generating machine learning model is trained to generate one or more failure causes based, at least in part, on one or more previously unseen images of ESP equipment. The training of the untrained failure cause-generating machine learning model can use the same training data as used to train the untrained categorizing machine learning model or different training data. For example, the training data for training the untrained failure cause-generating machine learning model may be images of ESP equipment along with the reliability reports. As another example, the failure causes specified in the reliability reports may be extracted along with the images in the reliability reports. Each sample of the training data might then consist of one or more images from a particular reliability report mapped to one or more failure causes from the same reliability report.

The example implementation might also comprise an untrained reliability report-generating machine learning model. The untrained reliability report-generating machine learning model is trained to generate reliability reports based, at least in part, on one or more previously unseen images of ESP equipment. The training of the untrained reliability report-generating machine learning model can use the same training data as used to train the untrained categorizing machine learning model or different training data. For example, the training data for training the untrained reliability report-generating machine learning model may be images of ESP equipment along with reliability reports containing the images of the ESP equipment.

The training process may result in a trained reliability report-generating machine learning model that can generate descriptive text, image captions, and failure causes based on one or more previously unseen images of ESP equipment.

1 FIG. 1 FIG. 100 102 101 102 104 114 116 104 114 116 130 132 101 102 106 is a diagrammatic illustration of an example well system, according to some implementations. In particular,depicts a well systemthat comprises a wellborein a formation. The wellboreincludes a casingand a number of perforations,in the casing. Each set of perforations,is made in a corresponding stage of a set of stagesandto allow reservoir fluids (i.e., oil, water, and gas) from the formationto flow into the wellboreand into the tubular string(the production tubing).

100 118 111 111 118 100 120 106 The well systemincludes a wellheadlocated on a pad. The padmay include a variety of equipment that varies depending on the stage of the operation, some of which may be part of the wellhead. The well systemalso includes ESP equipment. The ESP equipment (including the electrical submersible pump itself) may be part of or coupled with the tubular string.

2 FIG. 2 FIG. 200 202 204 206 202 203 212 205 204 208 210 206 is an illustration of an example system for training a categorizing machine learning module, according to some implementations. In particular,depicts an example systemincluding a computing system, training data, and a categorizing machine learning model. The computing systemincludes a training module, an object detection module, and a natural language processing module (“NLP processing module”). The training dataconsists of sets of samples data A through n (represented by sample data Aand sample data n). The categorizing machine learning modelcan implement operations to categorize images of ESP equipment.

208 210 Sample data Aincludes ESP equipment images A, ESP equipment metadata A, operational parameters A, and a reliability report A. Sample data nincludes ESP equipment images n, ESP equipment metadata n, operational parameters n, and a reliability report n. Actual implementations need not include all of the data depicted and may include additional data not depicted.

204 202 202 204 203 206 In operation, the training datais provided to, or retrieved by, the computing system. The computing systemprovides the training dataas input to the training module, which then executes one or more operations to train the categorizing machine learning model.

203 212 212 203 204 The training modulemay use, or work in conjunction with, the object detection module. For example, the object detection modulemay detect objects in the ESP equipment images A, such as impellers, shafts, and motors, and then provide indications of the detected objects to the training module. The detected objects may thus supplement the training data.

203 205 205 203 204 Similarly, the training modulemay use, or work in conjunction with, the NLP module. For example, the NLP modulemay identify and process text in captions embedded in the ESP equipment images A, ESP equipment metadata A, and reliability reports A, and then provide the processed text to the training module. The identified and processed text may thus supplement the training data.

203 206 206 Once training is complete, the training moduleoutputs the categorizing machine learning model. The categorizing machine learning modelcan include multiple trained machine learning models as well as code usable to perform the categorization operations described herein.

206 Although not required, the categorizing machine learning modelis typically persisted to a machine-readable storage medium, such as a hard drive or an object store in the cloud.

212 205 204 Actual implementations may not include an object detection moduleor NLP moduleand may include additional modules useful for processing and supplementing the training data.

3 FIG. 3 FIG. 300 302 304 306 302 303 305 304 308 310 306 is an illustration of an example system for training a caption-generating machine learning module, according to some implementations. In particular,depicts an example systemincluding a computing system, training data, and a caption-generating machine learning model. The computing systemincludes a training moduleand a natural language processing module (“NLP processing module”). The training dataconsists of sets of samples data A through n (represented by sample data Aand sample data n). The caption-generating machine learning modelcan implement operations to generate captions corresponding to images of ESP equipment.

308 310 Sample data Aincludes ESP equipment images with captions A and sample data nincludes ESP equipment images with captions n. Actual implementations need not include all of the data depicted and may include additional data not depicted. For example, the ESP equipment images may be embedded in a reliability report that includes text-based captions in proximity to the ESP equipment images. In such cases the sample data may be the reliability reports.

304 302 302 304 303 306 In operation, the training datais provided to, or retrieved by, the computing system. The computing systemprovides the training dataas input to the training module, which then executes one or more operations to train the caption-generating machine learning model.

303 305 305 303 304 305 303 304 The training modulemay use, or work in conjunction with, the NLP module. For example, the NLP modulemay identify and process text in captions embedded in the ESP equipment images A and provide the identified and processed captions to the training module. As another example, if the ESP equipment images provided in the training dataare embedded in reliability reports, the NLP modulemay identify portions of the reliability reports that refer to the images then process the corresponding text and provide the identified and processed text to the training module. The identified and processed text may thus supplement the training data.

303 306 306 Once training is complete, the training moduleoutputs the caption-generating machine learning model. The caption-generating machine learning modelcan include multiple trained machine learning models as well as code usable to perform the categorization operations described herein.

306 Although not required, the caption-generating machine learning modelis typically persisted to a machine-readable storage medium, such as a hard drive or an object store in the cloud.

205 304 Actual implementations may not include an NLP moduleand may include additional modules useful for processing and supplementing the training data.

4 FIG. 4 FIG. 400 402 404 406 402 403 412 405 404 408 410 406 is an illustration of an example system for training a failure cause-generating machine learning module, according to some implementations. In particular,depicts an example systemincluding a computing system, training data, and a failure cause-generating machine learning model. The computing systemincludes a training module, an object detection module, and a natural language processing module (“NLP processing module”). The training dataconsists of sets of samples data A through n (represented by sample data Aand sample data n). The failure cause-generating machine learning modelcan implement operations to generate failure causes associated with ESP equipment.

408 410 Sample data Aincludes ESP equipment images A, ESP equipment metadata A, operational parameters A, and a reliability report A. Sample data nincludes ESP equipment images n, ESP equipment metadata n, operational parameters n, and a reliability report n. Actual implementations need not include all of the data depicted and may include additional data not depicted.

404 402 402 404 403 406 In operation, the training datais provided to, or retrieved by, the computing system. The computing systemprovides the training dataas input to the training module, which then executes one or more operations to train the failure cause-generating machine learning model.

403 412 412 403 404 The training modulemay use, or work in conjunction with, the object detection module. For example, the object detection modulemay detect objects in the ESP equipment images A, such as impellers, shafts, and motors, and then provide indications of the detected objects to the training module. The detected objects may thus supplement the training data.

403 405 405 403 404 Similarly, the training modulemay use, or work in conjunction with, the NLP module. For example, the NLP modulemay identify and process text in captions embedded in the ESP equipment images A, ESP equipment metadata A, and reliability reports A, and then provide the processed text to the training module. The identified and processed text may thus supplement the training data.

403 406 406 Once training is complete, the training moduleoutputs the failure cause-generating machine learning model. The failure cause-generating machine learning modelcan include multiple trained machine learning models as well as code usable to perform the categorization operations described herein.

406 Although not required, the failure cause-generating machine learning modelis typically persisted to a machine-readable storage medium, such as a hard drive or an object store in the cloud.

412 405 404 Actual implementations may not include an object detection moduleor NLP moduleand may include additional modules useful for processing and supplementing the training data.

5 FIG. 5 FIG. 500 502 504 506 502 503 512 505 504 508 510 506 is an illustration of an example system for training a reliability-report-generating machine learning module, according to some implementations. In particular,depicts an example systemincluding a computing system, training data, and a reliability report-generating machine learning model. The computing systemincludes a training module, an object detection module, and a natural language processing module (“NLP processing module”). The training dataconsists of sets of samples data A through n (represented by sample data Aand sample data n). The reliability report-generating machine learning modelcan implement operations to generate reliability reports for ESP equipment.

508 510 Sample data Aincludes ESP equipment images A, ESP equipment metadata A, operational parameters A, and a reliability report A. Sample data nincludes ESP equipment images n, ESP equipment metadata n, operational parameters n, and a reliability report n. Actual implementations need not include all of the data depicted and may include additional data not depicted.

504 502 502 504 503 506 In operation, the training datais provided to, or retrieved by, the computing system. The computing systemprovides the training dataas input to the training module, which then executes one or more operations to train the reliability report-generating machine learning model.

503 512 512 503 504 The training modulemay use, or work in conjunction with, the object detection module. For example, the object detection modulemay detect objects in the ESP equipment images A, such as impellers, shafts, and motors, and then provide indications of the detected objects to the training module. The detected objects may thus supplement the training data.

503 505 505 503 504 Similarly, the training modulemay use, or work in conjunction with, the NLP module. For example, the NLP modulemay identify and process text in captions embedded in the ESP equipment images A, ESP equipment metadata A, and reliability reports A, and then provide the processed text to the training module. The identified and processed text may thus supplement the training data.

503 506 506 Once training is complete, the training moduleoutputs the reliability report-generating machine learning model. The reliability report-generating machine learning modelcan include multiple trained machine learning models as well as code usable to perform the categorization operations described herein.

506 Although not required, the reliability report-generating machine learning modelis typically persisted to a machine-readable storage medium, such as a hard drive or an object store in the cloud.

512 505 504 Actual implementations may not include an object detection moduleor NLP moduleand may include additional modules useful for processing and supplementing the training data.

6 FIG. 6 FIG. 600 602 612 614 616 618 620 depicts a system for analyzing images of ESP equipment using one or more machine learning models, according to some implementations. In particular,depicts a systemcomprising a computing system, input data, and a set of outputs comprising one or more image categories, one or more image captions, one or more ESP equipment failure causes, and an ESP equipment reliability report.

602 604 606 608 610 602 Computing systemincludes one or more machine learning models, depicted in this example as a categorizing machine learning model, a caption-generating machine learning model, a failure cause-generating machine learning model, and a reliability report-generating machine learning model. As noted below, each of the machine learning models of computing systemmay be included in, or work in conjunction with, one or more machine learning modules. Similarly, each machine learning model may actually be a collection of multiple machine learning modules.

612 622 624 626 624 626 The input dataincludes an ESP equipment image, ESP equipment metadata, and operational parameters. The ESP equipment metadatacan include any metadata or information associated with the ESP equipment, such as models and specifications associated with various components of the ESP equipment. The operational parametersmay include any parameters associated with the operation of the ESP equipment, such as the depth at which it was placed, the flow rate, and the type of fluid flowing through the ESP equipment.

612 602 612 612 In operation, the input datais provided to, or retrieved by, the computing system. The input datais then provided as input to the one or more machine learning models. The input datamay be transformed or have other operations applied to it prior to being provided as input to the one or more machine learning models or the one or more machine learning models may transform or perform the operations themselves.

612 612 604 622 606 622 608 618 610 620 After receiving the input data, each of the machine learning models performs one or more operations and produces an output based, at least in part, on the input data. In this example, the categorizing machine learning modeloutputs one or more image categories associated with the ESP equipment image; the caption-generating machine learning modeloutputs one or more image captions associated with the ESP equipment image; the failure cause-generating machine learning modeloutputs one or more ESP equipment failure causes; and the reliability report-generating machine learning modeloutputs an ESP equipment reliability report.

The output from the one or more machine learning models is typically output in a manner usable by any downstream components or users. For example, the output may be displayed on a user interface for review by a user, may be stored on a machine-readable medium for later reference, may be fed as input into one of the other machine learning models, etc.

Actual implementations need not use all of the illustrated machine learning models and thus may implement a subset of the illustrated machine learning models as well as implementing machine learning models not depicted. Similarly, actual implementations that implement multiple machine learning models may implement them on separate computing systems.

612 The input datacan vary depending on the specific machine learning models implements. For example, a particular caption-generating machine learning model may only accept ESP equipment images as input. Thus, an implementation that only uses a caption-generating machine learning model may only have ESP equipment images as input. Similarly, a reliability report-generating machine learning model may work better with multiple images of the ESP equipment and thus the input data may include multiple ESP equipment images.

7 FIG. 7 FIG. 2 FIG. 3 FIG. 4 FIG. 5 FIG. 700 202 302 402 502 700 700 702 depicts a flow diagram illustrating operations performed to train one or more machine learning models to work with ESP equipment images, according to some implementations. Operations depicted in the flowchartofcan be performed by one or more computing systems (e.g., computing systemof, computing systemof, computing systemof, computing systemof) using software, firmware, hardware, or any combination thereof. The operations of flowchartmay be described in reference to implementations described herein but the operations can be adapted to work with any suitable implementation. Operations of flowchartbegin at block.

702 At block, training data associated with ESP equipment is generated. The training data generation process may vary depending on the specific types of training data. For example, images may be extracted from text documents, images may be transformed from one format to another, structured text may be parsed, filtered, and transformed, etc. The training data may be formatted and stored in a variety of formats, such as in a relational database or one or more text files.

The generation of the training data may include augmenting source data. For example, an image may be run through an object detection module that identifies the subjects of the image and the output from the object detection module may be stored in manner indicating the association with the image.

As another example, data may be extracted from larger collections of data and stored separately. For example, the model number of a particular piece of ESP equipment may be extracted from a specifications document and then stored as part of a database entry associated with the piece of ESP equipment.

704 At block, one or more machine learning models are trained based, at least in part, on the training data. The particular technique(s) used to train the one or more machine learning models may vary depending on the type of machine learning model being trained.

706 At block, the one or more machine learning models are persisted to machine-readable media, such as a hard disk or cloud storage.

700 The operations of the flowchartcan be adapted and/or applied to train any of the machine learning models described herein.

8 FIG. 8 FIG. 6 FIG. 800 602 800 800 802 depicts a flow diagram illustrating operations for analyzing ESP equipment images using one or more trained machine learning models, according to some implementations. Operations depicted in the flowchartofcan be performed by one or more computing systems (e.g., computing systemof) using software, firmware, hardware, or any combination thereof. The operations of flowchartmay be described in reference to implementations described herein but the operations can be adapted to work with any suitable implementation. Operations of flowchartbegin at block.

802 602 612 612 6 FIG. At block, input data is provided to one or more trained machine learning models. The input data comprises at least an image of ESP equipment. The input data may be unmodified input data or one or more operations may be performed to transport raw input data into input data suitable for the one or more trained machine learning models. For example, the computing systemofreceives the input dataand provides the input datato the one or more machine learning models.

804 At block, indications of one or more categories of a plurality of categories are generated by the one or more trained machine learning models based, at least in part, on the image of the ESP equipment. The specific form that the indications take may vary depending on the one or more machine learning models used and how the machine learning models were trained. For example, the indications may be textual or numeric.

604 614 622 6 FIG. As an example, the categorizing machine learning modelinoutputs one or more image categoriesbased on the ESP equipment image.

806 606 616 6 FIG. At block, one or more captions are generated by the one or more trained machine learning models based, at least in part, on the image of the ESP equipment. As an example, the caption-generating machine learning modelofoutputs one or more image captions.

808 608 618 6 FIG. At block, one or more failure causes are generated by the one or more trained machine learning models based, at least in part, on the image of the ESP equipment. As an example, the failure cause-generating machine learning modelofoutputs one or more ESP equipment failure causes.

810 610 620 6 FIG. At block, a reliability report is generated by the one or more trained machine learning models based, at least in part, on the image of the ESP equipment. As an example, the reliability report-generating machine learning modelofoutputs the ESP equipment reliability report.

800 802 804 800 800 An actual implementation may not implement all operations depicted in the flowchart. For example, some implementations may only implement a categorizing machine learning model and thus may only implement the operations of blockand block. Thus, implementations may perform some of the operations depicted in the flowchart. Implementations may also perform additional operations that are not depicted in the flowchartor may not perform the operations in parallel. For example, some implementations may utilize the outputs of one or more of the machine learning models as input to other machine learning models. For example, captions generated by a caption-generating machine learning model may be usable by a failure cause-generating machine learning model to determine a failure cause.

The one or more machine learning models may be incorporated into one or more machine learning modules that are capable of performing one or more operations to support the machine learning models. For example, a machine learning module may perform data acquisition operations, data transformation operations, operations to load and or configure the one or more machine learning models, etc. A machine learning module may also be capable of performing one or more operations to train or retrain the incorporated machine learning models. In some implementations, the machine learning model is not incorporated into the machine learning module but works in conjunction with the machine learning module. The operations describe herein can be adapted for implementations in which a machine learning model is incorporated into one or more machine learning modules, implementations in which a machine learning model works in conjunction with one or more machine learning modules, or any other appropriate implementation. References herein to “machine learning models” should thus be interpreted to include program code and other resources used to support the operation of the machine learning models.

It should be noted that the descriptions herein describe one or more machine learning models generating one or more indications of a failure cause. However, the operations described herein may be applied to ESP equipment that has not failed. For example, if ESP equipment is retired (e.g., due to age) but has not actually failed, the operations described herein may be used to determine whether the retired ESP equipment exhibits signs of impending failure and what types of failures.

It should further be noted that although the descriptions herein may refer to machine learning modules making determinations or generating various outputs, it is understood that machine learning and similar techniques may actually generate probabilities associated with particular outputs. Further, the machine learning modules may be implemented such that probabilities over certain thresholds are presented without reference to their probabilities.

The term “ESP equipment” includes electrical submersible pumps themselves as well as any related equipment that supports the operation of the electrical submersible pumps themselves.

Although the descriptions above refer to images of ESP equipment, the subject matter is not so limited and may be adapted for different types of images. For example, the operations described herein may be applied to images created by devices and systems like scanning electron microscopes and other imaging devices, charts and graphs of sensor or measurement device outputs, etc.

While the aspects of the disclosure are described with reference to various implementations and exploitations, it will be understood that these aspects are illustrative and that the scope of the claims is not limited to them. In general, techniques for detecting subsurface conditions using tube waves as described herein may be implemented with facilities consistent with any hardware system or hardware systems. Many variations, modifications, additions, and improvements are possible.

Plural instances may be provided for components, operations or structures described herein as a single instance. Further, boundaries between various components, operations and data stores are somewhat arbitrary, and particular operations are illustrated in the context of specific illustrative configurations. Other allocations of functionality are envisioned and may fall within the scope of the disclosure. In general, structures and functionality presented as separate components in the example configurations may be implemented as a combined structure or component. Similarly, structures and functionality presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements may fall within the scope of the disclosure.

The flowcharts are provided to aid in understanding the illustrations and are not to be used to limit the scope of the claims. The flowcharts depict example operations that can vary within the scope of the claims. Additional operations may be performed; fewer operations may be performed; the operations may be performed in parallel; and the operations may be performed in a different order. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by program code. The program code may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable machine or apparatus.

Use of the phrase “at least one of” preceding a list with the conjunction “and” should not be treated as an exclusive list and should not be construed as a list of categories with one item from each category, unless specifically stated otherwise. A clause that recites “at least one of A, B, and C” can be infringed with only one of the listed items, multiple of the listed items, and one or more of the items in the list and another item not listed.

As used herein, the term “or” is inclusive unless otherwise explicitly noted. Thus, the phrase “at least one of A, B, or C” is satisfied by any element from the set {A, B, C} or any combination thereof, including multiples of any element.

9 FIG. 9 FIG. 9 FIG. 900 900 901 900 915 915 915 915 901 905 903 903 907 901 900 907 907 900 905 is a block diagram depicting an example computer, according to some implementations.depicts a computerfor ESP equipment failure analysis. The computerincludes a processor(possibly including multiple processors, multiple cores, multiple nodes, and/or implementing multi-threading, etc.). The computeralso includes an ESP equipment training and analysis unitwhich may perform the operations described herein. For example, the ESP equipment training and analysis unitmay generate training data associated with ESP equipment; train one or more machine learning models, based at least in part, on the training data; persist the one or more machine learning models to machine-readable media; provide input data including at least an image of ESP equipment, to the one or more machine learning models; and generate categories, captions, failure causes, or reliability reports based, at least in part, on the image of ESP equipment. Any one of the previously described functionalities may be partially (or entirely) implemented in hardware and/or on ESP equipment training and analysis unit. For example, the functionality may be implemented with an application specific integrated circuit, in logic implemented in the ESP equipment training and analysis unit, in a co-processor on a peripheral device or card, etc. Further, realizations may include fewer or additional components not illustrated in(e.g., video cards, audio cards, additional network interfaces, peripheral devices, etc.). The processorand the network interfaceare coupled to the bus. Although illustrated as being coupled to the bus, the memorymay be coupled to the processor. The computerincludes memory. The memorymay be system memory or any one or more possible realizations of machine-readable media. The computercan communicate via transmissions to and/or from remote devices via the network interfacein accordance with a network protocol corresponding to the type of network interface, whether wired or wireless and depending upon the carrying medium. In addition, a communication or transmission can involve other layers of a communication protocol and or communication protocol suites (e.g., transmission control protocol, Internet Protocol, user datagram protocol, virtual private network protocols, etc.).

Implementation 1: A method for electrical submersible pump equipment fault analysis, the method comprising training, using first training data, one or more machine learning models to categorize previously unseen images of electrical submersible pump equipment into one or categories of a plurality of categories; and training, using second training data, the one or more machine learning models to generate captions for the previously unseen images of electrical submersible pump equipment.

Implementation 2: The method according to any of the preceding Implementations, the method further comprising training, using third training data, the one or more machine learning models to determine a cause of a failure.

Implementation 3: The method according to any of the preceding Implementations, further comprising providing a plurality of images of electrical submersible pump equipment as input to a machine learning model of the one or more machine learning models, wherein the first training data, the second training data, and the third training data do not include the plurality of images of the electrical submersible pump equipment; and generating, using the machine learning model, a failure cause based, at least in part, on the plurality of images of the electrical submersible pump equipment.

Implementation 4: The method according to any of the preceding Implementations, further comprising generating, using the one or more machine learning models, a reliability report based, at least in part, on the plurality of images of electrical submersible pump equipment.

Implementation 5: The method according to any of the preceding Implementations, the method further comprising providing an image of electrical submersible pump equipment as input to a machine learning model of the one or more machine learning models, wherein the first training data does not include the image of the electrical submersible pump equipment; and generating, using the machine learning model, an indication of a category of the plurality of categories.

Implementation 6: The method according to any of the preceding Implementations, the method further comprising providing an image of electrical submersible pump equipment as input to a machine learning model of the one or more machine learning models, wherein the second training data does not include the image of the electrical submersible pump equipment; and generating, using the machine learning model, a caption corresponding to the image of the electrical submersible pump equipment.

Implementation 7: The method according to any of the preceding Implementations, wherein the first training data comprises at least a first plurality of images of electrical submersible pump equipment and the second training data comprises at least a second plurality of images of electrical submersible pump equipment with associated captions.

Implementation 8: A computing system comprising one or more processors; and one or more non-transitory computer-readable mediums including instructions which, when executed by the one or more processors, cause the one or more processors to execute one or more operations for electrical submersible pump equipment fault analysis, the instructions including instructions to train, using first training data, one or more machine learning models to categorize previously unseen images of electrical submersible pump equipment into one or categories of a plurality of categories; and instructions to train, using second training data, the one or more machine learning models to generate captions for the previously unseen images of electrical submersible pump equipment.

Implementation 9: The computing system according to any of the preceding Implementations, the instructions further including instructions to train, using third training data, the one or more machine learning models to determine a cause of a failure.

Implementation 10: The computing system according to any of the preceding Implementations, the instructions further including instructions to provide a plurality of images of electrical submersible pump equipment as input to a machine learning model of the one or more machine learning models, wherein the first training data, the second training data, and the third training data do not include the plurality of images of the electrical submersible pump equipment; and instructions to generate, using the machine learning model, a failure cause based, at least in part, on the plurality of images of the electrical submersible pump equipment.

Implementation 11: The computing system according to any of the preceding Implementations, the instructions further comprising instructions to generate, using the one or more machine learning models, a reliability report based, at least in part, on the plurality of images of electrical submersible pump equipment.

Implementation 12: The computing system according to any of the preceding Implementations, the instructions further including instructions to provide an image of electrical submersible pump equipment as input to a machine learning model of the one or more machine learning models, wherein the first training data does not include the image of the electrical submersible pump equipment; and instructions to generate, using the machine learning model, an indication of a category of the plurality of categories.

Implementation 13: The computing system according to any of the preceding Implementations, the instructions further including instructions to provide an image of electrical submersible pump equipment as input to a machine learning model of the one or more machine learning models, wherein the second training data does not include the image of the electrical submersible pump equipment; and instructions to generate, using the machine learning model, a caption corresponding to the image of the electrical submersible pump equipment.

Implementation 14: The computing system according to any of the preceding Implementations, wherein the first training data comprises at least a first plurality of images of electrical submersible pump equipment and the second training data comprises at least a second plurality of images of electrical submersible pump equipment with associated captions.

Implementation 15: One or more non-transitory computer-readable mediums including instructions which, when executed by a processor, cause the processor to execute one or more operations for electrical submersible pump equipment fault analysis, the instructions comprising instructions to train, using first training data, one or more machine learning models to categorize previously unseen images of electrical submersible pump equipment into one or categories of a plurality of categories; and instructions to train, using second training data, the one or more machine learning models to generate captions for the previously unseen images of electrical submersible pump equipment.

Implementation 16: The one or more non-transitory computer-readable mediums according to any of the preceding Implementations, the instructions further including instructions to train, using third training data, the one or more machine learning models to determine a cause of a failure.

Implementation 17: The one or more non-transitory computer-readable mediums according to any of the preceding Implementations, the instructions further including instructions to provide a plurality of images of electrical submersible pump equipment as input to a machine learning model of the one or more machine learning models, wherein the first training data, the second training data, and the third training data do not include the plurality of images of the electrical submersible pump equipment; and instructions to generate, using the machine learning model, a failure cause based, at least in part, on the plurality of images of the electrical submersible pump equipment.

Implementation 18: The one or more non-transitory computer-readable mediums according to any of the preceding Implementations, the instructions further comprising instructions to generate, using the one or more machine learning models, a reliability report based, at least in part, on the plurality of images of electrical submersible pump equipment.

Implementation 19: The one or more non-transitory computer-readable mediums according to any of the preceding Implementations, the instructions further including: instructions to provide an image of electrical submersible pump equipment as input to a machine learning model of the one or more machine learning models, wherein the first training data does not include the image of the electrical submersible pump equipment; and instructions to generate, using the machine learning model, an indication of a category of the plurality of categories.

Implementation 20: The one or more non-transitory computer-readable mediums according to any of the preceding Implementations, the instructions further including: instructions to provide an image of electrical submersible pump equipment as input to a machine learning model of the one or more machine learning models, wherein the second training data does not include the image of the electrical submersible pump equipment; and instructions to generate, using the machine learning model, a caption corresponding to the image of the electrical submersible pump equipment.